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import time
import os
import gradio as gr
from text_generation import Client
from conversation import get_default_conv_template
from transformers import AutoTokenizer
endpoint_url = os.environ.get("ENDPOINT_URL", "http://127.0.0.1:8080")
client = Client(endpoint_url, timeout=120)
eos_token = "</s>"
max_prompt_length = 4000
tokenizer = AutoTokenizer.from_pretrained("yentinglin/Taiwan-LLaMa-v1.0")
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
msg = gr.Textbox()
clear = gr.Button("Clear")
def user(user_message, history):
return "", history + [[user_message, None]]
def bot(history):
conv = get_default_conv_template("vicuna").copy()
roles = {"human": conv.roles[0], "gpt": conv.roles[1]} # map human to USER and gpt to ASSISTANT
for user, bot in history:
conv.append_message(roles['human'], user)
conv.append_message(roles["gpt"], bot)
msg = conv.get_prompt()
prompt_tokens = tokenizer.encode(msg)
length_of_prompt = len(prompt_tokens)
if length_of_prompt > max_prompt_length:
msg = tokenizer.decode(prompt_tokens[-max_prompt_length+1:])
history[-1][1] = ""
for response in client.generate_stream(
msg,
max_new_tokens=512,
):
if not response.token.special:
character = response.token.text
history[-1][1] += character
yield history
def generate_response(history, max_new_token=512, top_p=0.9, temperature=0.8, do_sample=True):
conv = get_default_conv_template("vicuna").copy()
roles = {"human": conv.roles[0], "gpt": conv.roles[1]} # map human to USER and gpt to ASSISTANT
for user, bot in history:
conv.append_message(roles['human'], user)
conv.append_message(roles["gpt"], bot)
msg = conv.get_prompt()
for response in client.generate_stream(
msg,
max_new_tokens=max_new_token,
top_p=top_p,
temperature=temperature,
do_sample=do_sample,
):
history[-1][1] = ""
# if not response.token.special:
character = response.token.text
history[-1][1] += character
print(history[-1][1])
time.sleep(0.05)
yield history
msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
bot, chatbot, chatbot
)
clear.click(lambda: None, None, chatbot, queue=False)
demo.queue()
demo.launch()
#
# with gr.Blocks() as demo:
# chatbot = gr.Chatbot()
# with gr.Row():
# with gr.Column(scale=4):
# with gr.Column(scale=12):
# user_input = gr.Textbox(
# show_label=False,
# placeholder="Shift + Enter傳送...",
# lines=10).style(
# container=False)
# with gr.Column(min_width=32, scale=1):
# submitBtn = gr.Button("Submit", variant="primary")
# with gr.Column(scale=1):
# emptyBtn = gr.Button("Clear History")
# max_new_token = gr.Slider(
# 1,
# 1024,
# value=128,
# step=1.0,
# label="Maximum New Token Length",
# interactive=True)
# top_p = gr.Slider(0, 1, value=0.9, step=0.01,
# label="Top P", interactive=True)
# temperature = gr.Slider(
# 0,
# 1,
# value=0.5,
# step=0.01,
# label="Temperature",
# interactive=True)
# top_k = gr.Slider(1, 40, value=40, step=1,
# label="Top K", interactive=True)
# do_sample = gr.Checkbox(
# value=True,
# label="Do Sample",
# info="use random sample strategy",
# interactive=True)
# repetition_penalty = gr.Slider(
# 1.0,
# 3.0,
# value=1.1,
# step=0.1,
# label="Repetition Penalty",
# interactive=True)
#
# params = [user_input, chatbot]
# predict_params = [
# chatbot,
# max_new_token,
# top_p,
# temperature,
# top_k,
# do_sample,
# repetition_penalty]
#
# submitBtn.click(
# generate_response,
# [user_input, max_new_token, top_p, top_k, temperature, do_sample, repetition_penalty],
# [chatbot],
# queue=False
# )
#
# user_input.submit(
# generate_response,
# [user_input, max_new_token, top_p, top_k, temperature, do_sample, repetition_penalty],
# [chatbot],
# queue=False
# )
#
# submitBtn.click(lambda: None, [], [user_input])
#
# emptyBtn.click(lambda: chatbot.reset(), outputs=[chatbot], show_progress=True)
#
# demo.launch() |